.png){kind=icon}
返回
Satellite Unsupervised Anomaly Detection Based on Deconvolution-Reconstructed Temporal Convolutional Autoencoder
.png){kind=icon}
PRE
AI
DOI：10.1109/TCE.2023.3306428.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Anomaly detection for orbiting satellites has become a paramount research focus in the aerospace domain. Data-driven methodologies, employing high-dimensional telemetry data, have exhibited significant potential for timely anomaly detection without the need for pre-existing models or rules. In this paper, we introduce a novel satellite anomaly detection method, the Deconvolution-reconstructed Temporal Convolutional Autoencoder (DRTCAE), which utilizes preprocessed telemetry data. The DRTCAE consists of a convolutional encoder and a deconvolutional decoder, each leveraging the parallelism of stacked Advanced Dilated Causal Convolutional (ADCC) blocks. This innovative architecture facilitates efficient multi-scale nonlinear transformations, which are crucial for extracting time-dependent features from various telemetry data sources and enabling accurate anomaly detection. Through extensive experiments with telemetry data from the Zhuhai-1 OVS3A satellite and an unspecified GEO satellite, our findings demonstrate that the DRTCAE-based approach outperforms traditional multivariate regression techniques. In conclusion, our results underscore the DRTCAE as an exceptionally proficient method for detecting anomalies in orbiting satellites, thereby contributing to the improvement of overall reliability and safety in space missions.
Keyword:
Satellites
Telemetry
Anomaly detection
Feature extraction
Convolution
Training
Data models
satellite telemetry data
temporal convolutional autoencoder
fault diagnosis
期刊
IF:
10.9
论文数:
5.3K
被引数:
6.8K
机构
引用论文
Structures of the cis and trans isomers of the bis(1-thia-4,7-diazacyclononane)cobalt(III) cation
.png){kind=icon}
PREAI
分享收藏Deep Generative Model Using Unregularized Score for Anomaly Detection With Heterogeneous Complexity
OAAI
OAAI分享收藏Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions基于用户行为和家居条件的智能家居运行异常检测
OAAI
OAAI分享收藏Research on the Efficiency of Wireless Power Transfer System Based on Multi-Auxiliary Transmitting Coils基于多辅助发射线圈的无线电能传输系统效率研究
PREAI
分享收藏
查看更多内容